Northern Trust and Microsoft announced the agent in a joint press release on Monday 9 November. The Chicago-based bank uses it when onboarding new clients to its Front Office Solutions service, which produces performance and analytics reports for clients such as foundations, pension funds and large family offices.

Pete Cherecwich, the bank’s chief operating officer, told CIO.com that a large institutional client can have huge volumes of data, particularly from alternative investments. Several years of transactions and cash flows can reach the bank in widely differing formats. The bank previously converted the data by hand before moving it into its reporting environment, CIO.com reported.

Rule library and human review

The bank’s financial experts and Microsoft engineers built a rule library for the agent, Microsoft’s news site reported. The library lets the agent recognise the same item even when clients’ workbooks give it different names. Cherecwich cited capital calls and valuations as examples. The team gave the model the industry’s vocabulary and ran it on anonymised data from several past clients.

The engineers came from Microsoft Frontier Company, which places its developers in customers’ teams. Microsoft launched the $2.5 billion unit in July, CIO.com reported. Microsoft used only anonymised data to improve the agent, and client data stayed under the bank’s control, the press release said.

The agent runs on Microsoft’s Azure cloud and uses the Microsoft Foundry AI platform. Analysts review and approve its output in Excel and SharePoint. ‘The agent does a lot of the heavy lifting, and then our people review the output and handle the things where there’s uncertainty,’ Cherecwich told CIO.com.

What the figures measure

The bank measured the time saving in real client cases after deploying the agent. The measurement covered data conversion, analyst review, exception handling and final validation. It compared the previous workflow’s baseline of about 2.5 weeks with the average duration of cases handled with the agent. The time fell by 83 per cent, the press release said. The number of cases measured was not disclosed.

Microsoft wrote on X that client onboarding fell from 2.5 weeks to three days. The press release and its footnote, however, limit the figure to data preparation. After preparation, the bank carries out its regular reconciliation before the client moves onto the service, Microsoft’s article said. The reconciliation compares the bank’s books and records with those of the previous administrator.

The quality figure comes from a separate measurement. The agent achieved an F1 score of 96.5 per cent in a controlled test of seven cases drawn from real but anonymised client workbooks. Its first, uncorrected output was compared with answers prepared by analysts, which the agent had not seen. F1 combines two things: how much of the data the agent produces is correct, and how much of the correct data the agent finds.

Microsoft’s article describes the remaining 3.5 per cent as data the agent is unsure how to classify, which is routed to humans for review. The F1 score does not, however, directly show what share of the data ends up with humans.

Cherecwich said in Microsoft’s article that the bank does not plan to let AI make $100 million payments on its own any time soon. The bank is also exploring the use of agents in its other products, the article said.